Name
Abstract | ||
|---|---|---|
Image haze removal techniques are extensively used in several outdoor applications. Lack of sufficient knowledge that is required to restore hazy images, the existing techniques usually use various attributes and assign constant values to these attributes. Unsuitable assignment to these attributes does not provide desired dehazing results. The primary objective of this review paper is to provide a structured outline of some well-known haze removal techniques. This paper also focuses on the methods which can assign optimal values to image dehazing attributes. The review has revealed that the meta-heuristic techniques can attain the optimistic haze removal parameters and also concurrently develops an optimistic objective function to estimate the depth map efficiently. Finally, this paper describes the various issues and challenges of image dehazing techniques, which are required to be further studied. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-017-5321-6 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Haze removal, Dark channel prior, Filtering, Supervised learning, Meta-heuristic techniques | Computer vision,Computer science,Filter (signal processing),Supervised learning,Artificial intelligence,Removal techniques,Depth map,Haze | Journal |
Volume | Issue | ISSN |
77 | 8 | 1380-7501 |
Citations | PageRank | References |
9 | 0.47 | 33 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dilbag Singh | 1 | 67 | 15.16 |
| Vijay Kumar | 2 | 229 | 21.59 |